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About GrADE A Graph Based Data Driven Solver F
This document proposes a new framework called Graph Attention Differential Equation (GrADE) for solving time-dependent nonlinear partial differential equations (PDEs). GrADE couples graph neural networks, neural ordinary differential equations, and attention mechanisms. It uses a graph neural network to model the spatial domain by handling unstructured data on irregular domains. A neural ODE models the temporal domain. The attention mechanism assigns more weight to important inputs/features to improve performance. The framework is tested on 1D and 2D Burgers' equations, demonstrating its ability to model PDEs and scale to larger domains without retraining.
Detailed Information
| Author: | ['Hari Madhavan Krishna Kumar'] |
|---|---|
| Publication Year: | 2022 |
| Pages: | 20 |
| Language: | English |
| Format: | |
| Price: | FREE |
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